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Annals of Operations Research

, Volume 235, Issue 1, pp 103–128 | Cite as

A leader–follower partially observed, multiobjective Markov game

  • Yanling ChangEmail author
  • Alan L. Erera
  • Chelsea C. WhiteIII
Article

Abstract

The intent of this research is to generate a set of non-dominated finite-memory policies from which one of two agents (the leader) can select a most preferred policy to control a dynamic system that is also affected by the control decisions of the other agent (the follower). The problem is described by an infinite horizon total discounted reward, partially observed Markov game (POMG). For each candidate finite-memory leader policy, we assume the follower, fully aware of the leader policy, determines a (perfect memory) policy that optimizes the follower’s (scalar) criterion. The leader–follower assumption allows the POMG to be transformed into a specially structured, partially observed Markov decision process that we use to determine the follower’s best response policy for a given leader policy. We then approximate the follower’s policy by a finite-memory policy. Each agent’s policy assumes that the agent knows its current and recent state values, its recent actions, and the current and recent possibly inaccurate observations of the other agent’s state. For each leader/follower policy pair, we determine the values of the leader’s criteria. We use a multi-objective genetic algorithm to create the next generation of leader policies based on the values of the leader criteria for each leader/follower policy pair in the current generation. Based on this information for the final generation of policies, we determine the set of non-dominated leader policies. We present an example that illustrates how these results can be used to support a manager of a liquid egg production process (the leader) in selecting a sequence of actions to maximize expected process productivity while mitigating the risk due to an attacker (the follower) who seeks to contaminate the process with a chemical or biological toxin.

Keywords

Dynamic programming Artificial intelligence Sequential decision making 

Notes

Acknowledgments

This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2010-ST-061-FD0001 through a grant awarded by the National Center for Food Protection and Defense at the University of Minnesota. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security or the National Center for Food Protection and Defense.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yanling Chang
    • 1
    Email author
  • Alan L. Erera
    • 1
  • Chelsea C. WhiteIII
    • 1
  1. 1.H. Milton Stewart School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA

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